Applied Sciences

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34 pages, 9114 KB

Open AccessArticle

An Interval-Valued Neutrosophic Framework: Improved VIKOR with a Preference-Aware AHP–Entropy Weight Method for Evaluating Scalp-Detection Algorithms

by Xin Chen, Wei Sun and Ruiqiu Zhang

Appl. Sci. 2025, 15(22), 11937; https://doi.org/10.3390/app152211937 - 10 Nov 2025

Viewed by 307

Abstract

Scalp image detection faces challenges such as limited evaluation dimensions, difficulties in quantifying user perception, and insufficient discriminative power of traditional assessment methods. To address these issues, this paper proposes a multi-attribute decision-making model for deep learning algorithm selection. The model integrates subjective [...] Read more.

Scalp image detection faces challenges such as limited evaluation dimensions, difficulties in quantifying user perception, and insufficient discriminative power of traditional assessment methods. To address these issues, this paper proposes a multi-attribute decision-making model for deep learning algorithm selection. The model integrates subjective and objective weighting through a hybrid approach, where natural language processing (NLP) techniques extract perceptual preferences from user reviews, and the Interval-valued neutrosophic set analytic hierarchy process (IVNS-AHP) and entropy weight method (EWM) are employed to determine subjective and objective weights, respectively. The combined weights are used within the IVNS-VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) framework, enhanced by a possibility distribution (PD) to improve discriminative capability. Experiments were conducted using multiple performance metrics, including Precision, Recall, mean Average Precision at IoU = 0.5 (mAP@50), F1 Score, frames per second (FPS), and Parameters, to evaluate mainstream scalp detection algorithms. The results demonstrate that YOLOv8n achieves the highest comprehensive ranking with strong stability across different decision preferences. Comparative analyses with TODIM (an acronym in Portuguese of interactive and multiple attribute decision-making), TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and fuzzy VIKOR variants confirm that the proposed PD-VIKOR method provides superior ranking stability and discriminative precision, offering a more reliable and robust evaluation under uncertainty. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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18 pages, 2957 KB

Open AccessArticle

Modelling a Fuzzy Logic-Based Multiple-Actuator Hydraulic Lifting and Positioning System

by Grzegorz Filo, Edward Lisowski, Paweł Lempa and Konrad Wisowski

Appl. Sci. 2025, 15(19), 10747; https://doi.org/10.3390/app151910747 - 6 Oct 2025

Viewed by 678

Abstract

This paper presents a fuzzy logic control strategy for synchronising the vertical lifting and positioning of a multi-actuator hydraulic system designed for a 360-ton movable platform. The primary focus is on achieving precise actuator movement coordination under uneven loading conditions without using external [...] Read more.

This paper presents a fuzzy logic control strategy for synchronising the vertical lifting and positioning of a multi-actuator hydraulic system designed for a 360-ton movable platform. The primary focus is on achieving precise actuator movement coordination under uneven loading conditions without using external reference systems or high-cost sensors. A mathematical model and a simulation environment were developed in MATLAB/Simulink with Fuzzy Logic Toolbox. Four fuzzy controller variants were evaluated regarding positioning accuracy, robustness, and compliance with dynamic constraints. The results demonstrate the effectiveness of the proposed control method, particularly when using Gaussian membership functions and PROD–PROBOR fuzzy operators. The system achieved sub-millimetre synchronisation accuracy even under 20% load imbalance. This work contributes to developing decentralised, sensor-light control strategies for large-scale hydraulic systems and offers a validated foundation for future experimental implementation in the PANDA particle detector project. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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26 pages, 1020 KB

Open AccessArticle

Evaluating Cybersecurity Measures for Smart Grids Under Uncertainty: A Picture Fuzzy SWARA–CODAS Approach

by Betul Kara, Ertugrul Ayyildiz, Bahar Yalcin Kavus and Tolga Kudret Karaca

Appl. Sci. 2025, 15(19), 10704; https://doi.org/10.3390/app151910704 - 3 Oct 2025

Viewed by 690

Abstract

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose [...] Read more.

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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25 pages, 768 KB

Open AccessArticle

Prioritizing Early-Stage Start-Up Investment Alternatives Under Uncertainty: A Venture Capital Perspective

by Mustafa Kellekci, Ufuk Cebeci and Onur Dogan

Appl. Sci. 2025, 15(18), 10060; https://doi.org/10.3390/app151810060 - 15 Sep 2025

Viewed by 1138

Abstract

Early-stage start-up selection is a critical yet challenging task for venture capital (VC) investors due to high uncertainty, limited historical data, and rapidly evolving business environments. Traditional evaluation processes often fall short in systematically handling multiple qualitative and uncertain factors that influence start-up [...] Read more.

Early-stage start-up selection is a critical yet challenging task for venture capital (VC) investors due to high uncertainty, limited historical data, and rapidly evolving business environments. Traditional evaluation processes often fall short in systematically handling multiple qualitative and uncertain factors that influence start-up success. As a result, there is a growing demand for robust decision models that can support VC firms in identifying promising early-stage ventures more accurately and consistently. This study presents a hybrid fuzzy multi-criteria decision-making approach tailored to the needs of venture capital investment under uncertainty. The model integrates expert judgment using the proportional spherical fuzzy AHP method to evaluate the relative importance of key dimensions. Then, spherical fuzzy TOPSIS is applied to rank investment alternatives based on their overall performance rankings. The proposed framework enables VC decision-makers to incorporate both subjective insights and data ambiguity in a structured and transparent way. It offers a practical tool to enhance the reliability of early-stage investment evaluations and improve the effectiveness of venture capital portfolio strategies. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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25 pages, 811 KB

Open AccessArticle

Logistics Companies’ Efficiency Analysis and Ranking by the DEA-Fuzzy AHP Approach

by Nikola Petrović, Vesna Jovanović, Dragan Marinković, Boban Nikolić and Saša Marković

Appl. Sci. 2025, 15(17), 9549; https://doi.org/10.3390/app15179549 - 30 Aug 2025

Cited by 1 | Viewed by 1452

Abstract

The logistics industry saw substantial growth in the second half of the 20th century, and logistics companies play a vital role in today’s modern market. Constant shifts in the market present challenges for logistics firms, which must find the optimal balance between achieved [...] Read more.

The logistics industry saw substantial growth in the second half of the 20th century, and logistics companies play a vital role in today’s modern market. Constant shifts in the market present challenges for logistics firms, which must find the optimal balance between achieved goals and utilized resources. The primary indicator that reflects this relationship is efficiency. Measuring and monitoring efficiency in logistics companies is extremely demanding because the final product is not a tangible item; instead, it often consists of transportation, storage, transloading, and forwarding services that require extensive resources. This paper focuses on measuring and improving efficiency. Numerous approaches and methods for evaluating the efficiency of logistics companies are examined. To measure and enhance efficiency, as well as rank companies based on operational efficiency, a three-phase DEA-fuzzy AHP model has been developed. This model was tested using a real-world example by analyzing the efficiency of ten logistics companies in the Republic of Serbia. The results of the analysis indicate the applicability of this model for measuring and improving the efficiency of logistics companies, as well as for their ranking. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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20 pages, 1356 KB

Open AccessArticle

A Novel Approach to the Vectorial Redefinition of Ordered Fuzzy Numbers for Improved Arithmetic and Directional Representation

by Hubert Zarzycki, Andrzej Żak and Jacek M. Czerniak

Appl. Sci. 2025, 15(13), 7427; https://doi.org/10.3390/app15137427 - 2 Jul 2025

Viewed by 510

Abstract

This paper presents a novel formulation of Ordered Fuzzy Numbers (OFNs), referred to as Vectorial Ordered Fuzzy Numbers (vOFNs). In contrast to the traditional definition based on a pair of functions, the vOFN framework employs a pair of vectors, offering a more concise [...] Read more.

This paper presents a novel formulation of Ordered Fuzzy Numbers (OFNs), referred to as Vectorial Ordered Fuzzy Numbers (vOFNs). In contrast to the traditional definition based on a pair of functions, the vOFN framework employs a pair of vectors, offering a more concise and structurally coherent representation. This reformulation addresses the key limitations of classical OFNs, such as non-convexity and difficulties in handling curvilinear boundaries during multiplication and division. The vOFN model retains compatibility with commonly used fuzzy number types—triangular, trapezoidal, and singleton—and preserves directional properties that are essential for modeling fuzzy trends. Furthermore, it simplifies comparison operations and supports a complete algebraic structure. Due to its mathematical consistency, low computational complexity, and ease of implementation, the vOFN framework is well-suited for applications in intelligent systems, particularly in domains that require reasoning under uncertainty. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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41 pages, 4581 KB

Open AccessArticle

An Integrated New Product Development Evaluation Model in an Interval Type-2 Fuzzy Environment for PLM Strategy Setting

by Sanja Puzović, Jasmina Vesić Vasović, Danijela Tadić and Dragan D. Milanović

Appl. Sci. 2025, 15(9), 5025; https://doi.org/10.3390/app15095025 - 30 Apr 2025

Viewed by 1447

Abstract

Product Lifecycle Management (PLM) provides a paradigmatic model that enables companies to operate more effectively in the face of shorter product lifecycles, global networking, and increasing complexity. However, despite strengthening the PLM initiative, companies still struggle to implement this concept. The limited results [...] Read more.

Product Lifecycle Management (PLM) provides a paradigmatic model that enables companies to operate more effectively in the face of shorter product lifecycles, global networking, and increasing complexity. However, despite strengthening the PLM initiative, companies still struggle to implement this concept. The limited results of current PLM implementations often stem from a lack of unique indicators or consistent methodologies that help companies prioritize their implementation efforts. This article proposes an approach to set a PLM strategy, focusing on enhancing company innovation potential by introducing a structured methodology capable of (i) capturing latent needs based on the normative-contingent New Product Development (NPD) evaluation model and (ii) quantifying the influence of various PLM functional aspects on NPD capability. The proposed methodology is based on the Quality Function Deployment (QFD) method, modified to overcome the limitations of the conventional approach, employing the Analytic Hierarchy Process (AHP) for prioritizing request attributes and the Evaluation based on Distance from Average Solution (EDAS) method for quality attribute importance ranking. Motivated by the arbitrary and vague nature of the decision-making environment in the PLM implementation projects, which introduces uncertainties that could be effectively managed by fuzzy logic, the study introduces Interval Type-2 Fuzzy Sets (IT2FSs) to minimize ambiguity and inconsistency in expressing and modeling preferences. The main study contribution pertains to generating quantitative and objective guidelines for adequately grounding a PLM strategy from the perspective of enhancing the company’s innovation potential. The findings of this study ultimately contribute to establishing an optimal model of the PLM concept implementation process, tailored to specific company requirements. Finally, an empirical case study demonstrates the effectiveness and practicality of the proposed approach. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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18 pages, 885 KB

Open AccessArticle

Risk Prioritizing with Weighted Failure Mode and Effects Analysis and Fuzzy Step-Wise Weight Assessment Ratio Analysis: An Application Software Service Provider Company in the Defense Industry

by Tulay Korkusuz Polat and Işılay Pamuk Candan

Appl. Sci. 2024, 14(24), 11573; https://doi.org/10.3390/app142411573 - 11 Dec 2024

Cited by 1 | Viewed by 2564

Abstract

With the development of technology, the need for software and software products to manage, control, and develop activities in many sectors is increasing daily. In order to create suitable software that will meet the needs of businesses and customers, the software application must [...] Read more.

With the development of technology, the need for software and software products to manage, control, and develop activities in many sectors is increasing daily. In order to create suitable software that will meet the needs of businesses and customers, the software application must be tested in detail before reaching the end user. For this reason, software testing processes are gaining importance in software development activities. This article discusses which errors are critical to solve in complex situations for the reliability and quality of the software product and the relationship between errors. In this study, the classical FMEA method was primarily used to identify and prioritize errors in an ongoing project of a company that provides software services in the defense industry. Later, to eliminate the shortcomings of the classical FMEA method, a new model, the weighted FMEA method (which calculates the risk priority score with five sub-severity components), was developed and applied. In the newly developed weighted FMEA method, the weights were determined by the fuzzy SWARA (Step-Wise Weight Assessment Ratio Analysis) method since the weights of the severity subcomponents were not the same. The risk priority number (RPN) of error types was calculated using classical FMEA and weighted FMEA. Since the RPNs calculated with weighted FMEA are calculated with more subcomponents, the chances of the RPNs’ errors appearing the same are much less than the RPNs calculated with classical FMEA. This situation indicates that the RPNs calculated with weighted FMEA are obtained from a more profound analysis. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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27 pages, 4906 KB

Open AccessArticle

Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation

by Xiaoli Li, Longlong Zhao, Hongzhong Li, Luyi Sun, Pan Chen, Ruixia Jiang and Jinsong Chen

Appl. Sci. 2024, 14(23), 11227; https://doi.org/10.3390/app142311227 - 2 Dec 2024

Viewed by 1225

Abstract

High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully [...] Read more.

High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. It extends Voronoi tessellation from hard to fuzzy, and proposes a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to Voronoi polygons and polygons fuzzily belong to clusters. The objective function is established based on the hierarchical fuzzy membership model by fully considering the transitivity of fuzziness between different levels. Then, the optimal classification result can be obtained by the fuzzy comprehensive decision theory under the best parameter solution. The first level retains the flexibility of pixels while modeling spatial constraints. The second level determines which class the polygon belongs to under the constraint of the first level. It provides an effective way of balancing noise resistance and boundary fitting. In addition, the Voronoi tessellation is explicitly expressed in the objective function in the form of the mathematical model, which allows it to obtain the optimal value through analytical solutions instead of the previous random sampling method. It greatly increases the convergence speed of the algorithm. Experiments have been performed on simulated and several remote sensing images with seven comparing algorithms to demonstrate the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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21 pages, 1238 KB

Open AccessArticle

A Consensus Framework for Evaluating Dispute Resolution Alternatives in International Law Using an Interval-Valued Type-2 Fuzzy TOPSIS Approach

by Ibrahim Yilmaz and Hatice Kubra Ecemis Yilmaz

Appl. Sci. 2024, 14(23), 11046; https://doi.org/10.3390/app142311046 - 27 Nov 2024

Cited by 6 | Viewed by 1835

Abstract

This research is motivated by the arbitrary nature of decision-making environments and the dynamic changes in decision patterns, particularly in international dispute resolution. These challenges introduce uncertainties that could be effectively managed by fuzzy logic, which provides a robust framework for evaluating alternatives [...] Read more.

This research is motivated by the arbitrary nature of decision-making environments and the dynamic changes in decision patterns, particularly in international dispute resolution. These challenges introduce uncertainties that could be effectively managed by fuzzy logic, which provides a robust framework for evaluating alternatives under multiple criteria. In this study, an Interval-Valued Type-2 Fuzzy TOPSIS approach is proposed to assess various dispute resolution methods, including negotiation, good offices, mediation, international inquiry, conciliation, international organization, arbitration, and international jurisdiction. Common criteria are determined by examining academic literature and by interviewing relevant experts.—cost-efficiency, duration, impartiality, binding nature, and generalizability are considered essential in determining the best resolution method. The proposed method allows for a nuanced evaluation by incorporating both primary and secondary levels of uncertainty, enabling decision-makers to determine the best alternative solution more reliably. This method’s application extends not only to the international law field but also to industrial engineering, where complex, uncertain decision environments require similarly sophisticated multicriteria decision-making tools. By systematically analyzing these resolution methods, this study aims to provide a structured, quantifiable approach that enhances the decision-making process for both international legal practitioners and engineers working with uncertain and dynamic systems. The results of this study ultimately contribute to improved decision-making outcomes and greater efficiency in multidisciplinary problem solving. The assessments of experts in international law, international relations, and political science in their respective fields of expertise have been gathered to form a consensus. This study contributes to the literature as it is the pioneering application of fuzzy multicriteria decision-making techniques in the field of international law. The results of this study imply that the best option from the different decision-maker evaluations is international jurisdiction. Consequently, the utilization of multicriteria decision-making tools can result in more informed and effective decisions in complex and uncertain situations, which is advantageous to both legal practitioners and engineers. Additionally, incorporating different disciplines can help streamline the decision-making process and improve overall efficiency in solving multidisciplinary problems. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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21 pages, 2448 KB

Open AccessArticle

Prioritization of Personal Protective Equipment Plans for Construction Projects Based on an Integrated Analytic Network Process and Fuzzy VIKOR Method

by Haifeng Jin and Paul M. Goodrum

Appl. Sci. 2024, 14(21), 9904; https://doi.org/10.3390/app14219904 - 29 Oct 2024

Cited by 1 | Viewed by 2948

Abstract

The risk of both fatal accidents and non-fatal injuries in the construction industry is significantly high in most countries. To reduce this construction safety risk, the proper use of personal protective equipment (PPE) is one of the major measures on the jobsite. In [...] Read more.

The risk of both fatal accidents and non-fatal injuries in the construction industry is significantly high in most countries. To reduce this construction safety risk, the proper use of personal protective equipment (PPE) is one of the major measures on the jobsite. In this research, in order to comprehensively assess the PPE plans, a three-phase framework was proposed to identify the optimal solution for PPE planning from a set of alternatives. As a result, four main criteria and fifteen sub-criteria were identified based on a systematic literature review, and a decision-making model integrating the analytic network process (ANP) and VIekriterijumsko KOmpromisno Rangiranje was developed. As the assessment information in the survey was incomplete and vague, the fuzzy sets theory was adopted to transform the linguistic terms into fuzzy numbers for evaluation. The model further calculated the weight of each criterion and prioritized the potential PPE plan alternatives. Finally, the presented model was implemented in a case study to verify its feasibility and applicability for practical construction management. The proposed method enables the selection of the most compromising solution as the optimal PPE plan. This research assists decision-makers and safety planners at construction workplaces to improve the overall safety performance and reduce accident risks, which significantly contributes to construction safety management and practice. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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20 pages, 10923 KB

Open AccessArticle

Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form

by Shijie Wang, Shutao Zhang, Jianning Su, Zhiqiang Yang, Shifeng Liu, Aimin Zhou, Wenjin Yang and Kai Qiu

Appl. Sci. 2024, 14(13), 5728; https://doi.org/10.3390/app14135728 - 30 Jun 2024

Cited by 1 | Viewed by 1625

Abstract

Current design decision-making methods ignore the fuzzy relationship between Kansei images, and the use of constant weights reduces the accuracy of cognitive evaluation results. To solve these problems, this paper proposes a grey decision-making method for product form driven by the fuzzy relationship [...] Read more.

Current design decision-making methods ignore the fuzzy relationship between Kansei images, and the use of constant weights reduces the accuracy of cognitive evaluation results. To solve these problems, this paper proposes a grey decision-making method for product form driven by the fuzzy relationship between Kansei images. First, according to the initial weight of the Kansei images, variable weight theory is used to determine the Kansei image variable weights of the samples, and the variable weight comprehensive evaluation results for each sample are obtained. Then, based on the correlation and angle of the Kansei images, a cobweb diagram is drawn to represent the fuzzy relationship between the Kansei images of each sample. Combined with the cobweb grey target decision-making model (CGTDM) for multiple Kansei images, decision coefficients are calculated. The decision coefficients are compared and ranked to determine the relatively optimal design reference sample. Finally, the constructed model is compared with the CGTDM for multiple Kansei images and TOPSIS. The results show that the difference coefficient of the proposed method is the largest, and it can reflect the decision-making thinking of the designers and improve the discrimination among the decision-making results to a certain extent. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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18 pages, 289 KB

Open AccessArticle

Combining the Fuzzy Analytic Hierarchy Process Method with the Weighted Aggregated Sum Product Assessment Method to Address Internet Platform Selection Problems in an Environment with Incomplete Information

by Kuei-Hu Chang, Hsin-Hung Lai and Bo-Jiun Hung

Appl. Sci. 2024, 14(11), 4390; https://doi.org/10.3390/app14114390 - 22 May 2024

Cited by 2 | Viewed by 3252

Abstract

With the advancement of information technology, the Internet is pivotal in today’s society, serving as a global connectivity platform. Leveraging Internet technology within an enterprise can improve operational efficiency and curtail costs. However, traditional Internet platform selection methods cannot simultaneously handle quantitative and [...] Read more.

With the advancement of information technology, the Internet is pivotal in today’s society, serving as a global connectivity platform. Leveraging Internet technology within an enterprise can improve operational efficiency and curtail costs. However, traditional Internet platform selection methods cannot simultaneously handle quantitative and qualitative information, fuzzy semantics, and incomplete expert-provided information. To address these limitations, this study integrated the fuzzy analytic hierarchy process (FAHP) and the weighted aggregated sum product assessment (WASPAS) approaches to tackle Internet platform selection problems within an incomplete information environment. To demonstrate the validity of this research approach, this study utilized a construction industry Internet platform selection case to confirm the efficacy of the proposed novel fuzzy analytic hierarchy process-based method. Comparative analysis against the weighted sum model (WSM), weighted product model (WPM), FAHP, and typical WASPAS approaches was conducted with numerical verification, revealing that the proposed method in this study effectively manages comprehensive information and yields more rational outcomes for construction industry Internet platforms. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

17 pages, 4183 KB

Open AccessArticle

Bayesian Linguistic Conditional System as an Attention Mechanism in a Failure Mode and Effect Analysis

by Roberto Baeza-Serrato

Appl. Sci. 2024, 14(3), 1126; https://doi.org/10.3390/app14031126 - 29 Jan 2024

Cited by 1 | Viewed by 1494

Abstract

Fuzzy Inference System behavior can be described qualitatively using a natural language, which is known as the expert-driven approach to handling non-statistical uncertainty. Generally, practical applications involve conceptualizing the problem by integrating linguistic uncertainty and using data by integrating stochastic uncertainty. The proposed [...] Read more.

Fuzzy Inference System behavior can be described qualitatively using a natural language, which is known as the expert-driven approach to handling non-statistical uncertainty. Generally, practical applications involve conceptualizing the problem by integrating linguistic uncertainty and using data by integrating stochastic uncertainty. The proposed probabilistic fuzzy system uses the Gaussian Density Function (GDF) to assign a probability to input variables integrating stochastic uncertainty. In addition, a linguistic interpretation is used to project various categories of the GDF integrating linguistic uncertainty. Likewise, one of the relevant aspects of the proposal is to weigh each input variable according to the heuristic interpretation that determines the probability assigned to each of them a priori. Therefore, the main contribution of the research focuses on using the Bayesian Linguistic Conditional System (BLCS) as a mechanism of attention to relate the categories of the different input variables and find their posterior-weighted probability at a normalization stage. Finally, the knowledge base is established through linguistic rules, and the system’s output is a Bayesian classifier multiplying its normalized posterior conditional probabilities. The highest probability value of the knowledge base is identified, and the Risk Priority Number Weighted (RPNW) is determined using their respective posterior-normalized probabilities for each input variable. The results are expressed on a simple and precise scale from 1 to 10. They are compared with the Risk Priority Number (RPN), which results in a Failure Mode and Effect Analysis (FMEA). They show similar behaviors for multiple combinations in the evaluations while highlighting different scales. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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31 pages, 7579 KB

Open AccessArticle

Selection of Optimal Segmentation Algorithm for Satellite Images by Intuitionistic Fuzzy PROMETHEE Method

by Edgaras Janusonis, Giruta Kazakeviciute-Januskeviciene and Romualdas Bausys

Appl. Sci. 2024, 14(2), 644; https://doi.org/10.3390/app14020644 - 12 Jan 2024

Cited by 5 | Viewed by 2427

Abstract

The combination of MCDM and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. Since no single segmentation algorithm can achieve [...] Read more.

The combination of MCDM and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. Since no single segmentation algorithm can achieve the best results on satellite image datasets, it is essential to determine the most appropriate segmentation algorithm for each satellite image, the content of which can be characterized by relevant visual features. In this research, we proposed a set of visual criteria representing the fundamental aspects of satellite image segmentation. The segmentation algorithms chosen for testing were evaluated for their performance against each criterion. We introduced a new method to create a decision matrix for each image using fuzzy fusion, which combines the image content vector and the evaluation matrix of the studied segmentation algorithms. An extension of the Preference Ranking Organization Method Enrichment Evaluation (PROMETHEE) using intuitive fuzzy sets (IFSs) was applied to solve this problem. The results acquired by the proposed methodology were validated by comparing them with those obtained in expert ratings and by performing a sensitivity analysis. Full article

(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)

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